# coding:utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('../mnist', one_hot=True)    # 导入mnist数据集


x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])   # x,y均为占位符


def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)   # 通过截断的正态分布函数产生随机值来初始化权重W
    return tf.Variable(initial)


def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)    # 初始化偏置b
    return tf.Variable(initial)


def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')   # 使用x作为输入,W作为卷积核,步长为1,考虑边界初始化卷积函数


def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                          strides=[1, 2, 2, 1], padding='SAME')     # 使用x作为输入,池化窗口为两行两列的矩阵,步长为2,考虑边界初始化池化函数


# 第一层卷积
W_conv1 = weight_variable([5, 5, 1, 32])   # 随机初始化权重,前两个维度是patch的大小,接着是输入通道的数目,最后是输出通道的数目,一共32个特征
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1, 28, 28, 1])   # 改变x的形状,使得x变为28*28的矩阵,-1代表NONE
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)   # ReLU激活函数
h_pool1 = max_pool_2x2(h_conv1)     # max pooling池化

# 第二层卷积
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

# 全连接层
W_fc1 = weight_variable([7 * 7 * 64, 1024])   # 随机初始化权重和偏重,图片尺寸减小到7*7,加入一个有1024个神经元的全连接层
b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])    # 把池化层reshape成一些向量
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
# tf.nn.dropout操作除了可以屏蔽神经元的输出外，还会自动处理神经元输出值的scale,减小过拟合
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
# 添加一个softmax层
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
cross_entropy = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))    # 采用softmax回归和求取交叉熵
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)      # 采用Adam优化器的梯度下降函数,以求取测试图片和正确标签最小的差值
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))     # 比较测试图像和正确标签中的索引值是否一样
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))     # 求取平均值来得出正确率

saver = tf.train.Saver()

# 开启会话
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(20000):    # 测试20000个数据
        batch = mnist.train.next_batch(50)     # 每个batch的大小为50个数据
        if i % 100 == 0:
            # 评估模型准确度,此阶段不使用Droupout
            train_accuracy = accuracy.eval(feed_dict={
                x: batch[0], y_: batch[1], keep_prob: 1.0})
            print('step %d, training accuracy %g' % (i, train_accuracy))
            # 训练模型，此阶段使用50%的Dropout
        train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
    saver.save(sess, '../savemodel/model.ckpt')    # 模型保存的位置

    print('test accuracy %g' % accuracy.eval(feed_dict={
        x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
